Overview

Dataset statistics

Number of variables18
Number of observations17416
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory144.0 B

Variable types

DateTime1
Numeric11
Categorical6

Alerts

temp is highly overall correlated with dwpt and 4 other fieldsHigh correlation
dwpt is highly overall correlated with temp and 3 other fieldsHigh correlation
rhum is highly overall correlated with temp and 1 other fieldsHigh correlation
hour_sin is highly overall correlated with is_night and 1 other fieldsHigh correlation
hour_cos is highly overall correlated with is_night and 1 other fieldsHigh correlation
month_sin is highly overall correlated with temp and 3 other fieldsHigh correlation
month_cos is highly overall correlated with temp and 4 other fieldsHigh correlation
lockdown2 is highly overall correlated with seasonHigh correlation
season is highly overall correlated with temp and 5 other fieldsHigh correlation
is_night is highly overall correlated with hour_sin and 1 other fieldsHigh correlation
lockdown1 is highly overall correlated with seasonHigh correlation
prcp has 15318 (88.0%) zerosZeros
hour_sin has 728 (4.2%) zerosZeros

Reproduction

Analysis started2022-11-29 16:35:39.568939
Analysis finished2022-11-29 16:36:28.654545
Duration49.09 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

date
Date

Distinct8750
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size136.2 KiB
Minimum2020-01-01 00:00:00
Maximum2022-01-01 22:00:00
2022-11-29T17:36:28.953717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:29.244795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temp
Real number (ℝ)

Distinct424
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.23444
Minimum-6.4
Maximum38.8
Zeros10
Zeros (%)0.1%
Negative209
Negative (%)1.2%
Memory size136.2 KiB
2022-11-29T17:36:29.610915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.4
5-th percentile3.1
Q18.1
median12.7
Q318.1
95-th percentile25.1
Maximum38.8
Range45.2
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8724803
Coefficient of variation (CV)0.5192876
Kurtosis-0.2540642
Mean13.23444
Median Absolute Deviation (MAD)4.9
Skewness0.30449152
Sum230491
Variance47.230986
MonotonicityNot monotonic
2022-11-29T17:36:29.919644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.7 125
 
0.7%
8.2 123
 
0.7%
8.8 122
 
0.7%
8 120
 
0.7%
9.8 118
 
0.7%
9.1 115
 
0.7%
8.5 111
 
0.6%
9.2 110
 
0.6%
10.3 109
 
0.6%
8.9 109
 
0.6%
Other values (414) 16254
93.3%
ValueCountFrequency (%)
-6.4 1
 
< 0.1%
-6.3 1
 
< 0.1%
-6.1 3
< 0.1%
-6 2
< 0.1%
-5.9 2
< 0.1%
-5.7 2
< 0.1%
-5.5 1
 
< 0.1%
-5.4 1
 
< 0.1%
-5.3 1
 
< 0.1%
-5.2 3
< 0.1%
ValueCountFrequency (%)
38.8 1
< 0.1%
38.7 2
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38.1 1
< 0.1%
37.5 1
< 0.1%
37.3 1
< 0.1%
37.2 1
< 0.1%
37 1
< 0.1%
36.7 1
< 0.1%

dwpt
Real number (ℝ)

Distinct350
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2219626
Minimum-17.1
Maximum22.6
Zeros71
Zeros (%)0.4%
Negative1504
Negative (%)8.6%
Memory size136.2 KiB
2022-11-29T17:36:30.253429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-17.1
5-th percentile-1.6
Q13.4
median7.4
Q311.4
95-th percentile15.5
Maximum22.6
Range39.7
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.4700887
Coefficient of variation (CV)0.75742413
Kurtosis0.16176853
Mean7.2219626
Median Absolute Deviation (MAD)4
Skewness-0.38718837
Sum125777.7
Variance29.92187
MonotonicityNot monotonic
2022-11-29T17:36:30.561610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 150
 
0.9%
6.5 145
 
0.8%
6.2 141
 
0.8%
6.9 138
 
0.8%
5.7 137
 
0.8%
6.4 133
 
0.8%
8.3 132
 
0.8%
7.7 132
 
0.8%
6.1 131
 
0.8%
8.5 130
 
0.7%
Other values (340) 16047
92.1%
ValueCountFrequency (%)
-17.1 1
< 0.1%
-16.3 1
< 0.1%
-16.1 1
< 0.1%
-15.9 1
< 0.1%
-15.5 1
< 0.1%
-15 1
< 0.1%
-14.8 1
< 0.1%
-14.7 1
< 0.1%
-14.5 1
< 0.1%
-14.1 1
< 0.1%
ValueCountFrequency (%)
22.6 1
 
< 0.1%
21.5 1
 
< 0.1%
20.9 1
 
< 0.1%
20.8 1
 
< 0.1%
20.7 1
 
< 0.1%
20.3 1
 
< 0.1%
20.1 1
 
< 0.1%
20 3
< 0.1%
19.8 4
< 0.1%
19.7 4
< 0.1%

rhum
Real number (ℝ)

Distinct83
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.789102
Minimum17
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.2 KiB
2022-11-29T17:36:30.907692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile37
Q157
median73
Q384
95-th percentile93
Maximum100
Range83
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.50213
Coefficient of variation (CV)0.25078601
Kurtosis-0.54826941
Mean69.789102
Median Absolute Deviation (MAD)13
Skewness-0.54654029
Sum1215447
Variance306.32457
MonotonicityNot monotonic
2022-11-29T17:36:31.646757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 444
 
2.5%
78 432
 
2.5%
80 429
 
2.5%
84 408
 
2.3%
88 407
 
2.3%
82 400
 
2.3%
85 400
 
2.3%
75 399
 
2.3%
79 398
 
2.3%
87 392
 
2.3%
Other values (73) 13307
76.4%
ValueCountFrequency (%)
17 1
 
< 0.1%
19 4
 
< 0.1%
20 8
 
< 0.1%
21 12
 
0.1%
22 19
0.1%
23 15
0.1%
24 28
0.2%
25 26
0.1%
26 28
0.2%
27 34
0.2%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 23
 
0.1%
98 60
 
0.3%
97 108
 
0.6%
96 168
1.0%
95 186
1.1%
94 245
1.4%
93 354
2.0%
92 364
2.1%
91 353
2.0%

prcp
Real number (ℝ)

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.092403537
Minimum0
Maximum18
Zeros15318
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size136.2 KiB
2022-11-29T17:36:31.954530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.53781413
Coefficient of variation (CV)5.8202765
Kurtosis424.70743
Mean0.092403537
Median Absolute Deviation (MAD)0
Skewness16.754174
Sum1609.3
Variance0.28924404
MonotonicityNot monotonic
2022-11-29T17:36:32.288256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 15318
88.0%
0.2 636
 
3.7%
0.4 278
 
1.6%
0.1 262
 
1.5%
0.6 179
 
1.0%
1 166
 
1.0%
0.8 148
 
0.8%
2 105
 
0.6%
0.3 65
 
0.4%
3 53
 
0.3%
Other values (33) 206
 
1.2%
ValueCountFrequency (%)
0 15318
88.0%
0.1 262
 
1.5%
0.2 636
 
3.7%
0.3 65
 
0.4%
0.4 278
 
1.6%
0.5 47
 
0.3%
0.6 179
 
1.0%
0.7 23
 
0.1%
0.8 148
 
0.8%
0.9 17
 
0.1%
ValueCountFrequency (%)
18 2
< 0.1%
17 3
< 0.1%
16 1
 
< 0.1%
11 3
< 0.1%
10 1
 
< 0.1%
9 3
< 0.1%
8 1
 
< 0.1%
7 3
< 0.1%
6 3
< 0.1%
5.1 1
 
< 0.1%

wdir
Real number (ℝ)

Distinct116
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean185.78026
Minimum0
Maximum360
Zeros40
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size136.2 KiB
2022-11-29T17:36:32.652442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q180
median210
Q3260
95-th percentile340
Maximum360
Range360
Interquartile range (IQR)180

Descriptive statistics

Standard deviation102.27287
Coefficient of variation (CV)0.55050452
Kurtosis-1.0776468
Mean185.78026
Median Absolute Deviation (MAD)70
Skewness-0.22861904
Sum3235549
Variance10459.74
MonotonicityNot monotonic
2022-11-29T17:36:32.967904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 1102
 
6.3%
210 974
 
5.6%
200 881
 
5.1%
230 844
 
4.8%
30 841
 
4.8%
190 763
 
4.4%
40 731
 
4.2%
50 646
 
3.7%
260 606
 
3.5%
280 573
 
3.3%
Other values (106) 9455
54.3%
ValueCountFrequency (%)
0 40
 
0.2%
10 465
2.7%
20 526
3.0%
23 1
 
< 0.1%
25 1
 
< 0.1%
26 2
 
< 0.1%
27 2
 
< 0.1%
28 2
 
< 0.1%
29 1
 
< 0.1%
30 841
4.8%
ValueCountFrequency (%)
360 430
2.5%
350 333
1.9%
346 1
 
< 0.1%
344 1
 
< 0.1%
343 1
 
< 0.1%
340 356
2.0%
337 1
 
< 0.1%
333 1
 
< 0.1%
332 1
 
< 0.1%
330 468
2.7%

wspd
Real number (ℝ)

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.307677
Minimum0
Maximum44.6
Zeros40
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size136.2 KiB
2022-11-29T17:36:33.282698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6
Q17.6
median11.2
Q314.8
95-th percentile20.5
Maximum44.6
Range44.6
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation5.2922299
Coefficient of variation (CV)0.46802097
Kurtosis1.3978596
Mean11.307677
Median Absolute Deviation (MAD)3.6
Skewness0.86633274
Sum196934.5
Variance28.007697
MonotonicityNot monotonic
2022-11-29T17:36:33.617068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 2560
14.7%
7.6 2396
13.8%
11.2 2257
13.0%
5.4 2236
12.8%
13 1981
11.4%
14.8 1599
9.2%
16.6 1106
6.4%
18.4 913
 
5.2%
3.6 907
 
5.2%
20.5 455
 
2.6%
Other values (55) 1006
 
5.8%
ValueCountFrequency (%)
0 40
 
0.2%
1.8 141
 
0.8%
3.6 907
 
5.2%
4.7 1
 
< 0.1%
5.4 2236
12.8%
5.8 1
 
< 0.1%
6.5 1
 
< 0.1%
6.8 1
 
< 0.1%
7.6 2396
13.8%
7.9 2
 
< 0.1%
ValueCountFrequency (%)
44.6 1
 
< 0.1%
42.5 1
 
< 0.1%
40.7 5
 
< 0.1%
38.9 5
 
< 0.1%
37.1 5
 
< 0.1%
35.6 1
 
< 0.1%
35.3 6
 
< 0.1%
33.5 16
 
0.1%
31.7 31
0.2%
29.5 44
0.3%

pres
Real number (ℝ)

Distinct624
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.0269
Minimum972.2
Maximum1047.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.2 KiB
2022-11-29T17:36:33.929550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum972.2
5-th percentile1000.4
Q11012
median1017.9
Q31023.1
95-th percentile1030.5
Maximum1047.8
Range75.6
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation9.2914833
Coefficient of variation (CV)0.0091359268
Kurtosis1.1378415
Mean1017.0269
Median Absolute Deviation (MAD)5.6
Skewness-0.58327778
Sum17712540
Variance86.331661
MonotonicityNot monotonic
2022-11-29T17:36:34.353448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1021 109
 
0.6%
1018.7 105
 
0.6%
1021.3 102
 
0.6%
1021.2 99
 
0.6%
1019.9 98
 
0.6%
1022 98
 
0.6%
1021.7 97
 
0.6%
1017.9 97
 
0.6%
1020.4 97
 
0.6%
1017.8 96
 
0.6%
Other values (614) 16418
94.3%
ValueCountFrequency (%)
972.2 1
< 0.1%
972.5 1
< 0.1%
972.7 1
< 0.1%
973.4 1
< 0.1%
973.6 1
< 0.1%
974.1 1
< 0.1%
974.8 1
< 0.1%
975.6 1
< 0.1%
975.9 2
< 0.1%
976.1 1
< 0.1%
ValueCountFrequency (%)
1047.8 1
 
< 0.1%
1047.6 1
 
< 0.1%
1047.4 1
 
< 0.1%
1047.1 1
 
< 0.1%
1047 1
 
< 0.1%
1046.9 1
 
< 0.1%
1046.6 1
 
< 0.1%
1046.5 3
< 0.1%
1046.4 1
 
< 0.1%
1046.2 2
< 0.1%

holiday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.2 KiB
0
16865 
1
 
551

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17416
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 16865
96.8%
1 551
 
3.2%

Length

2022-11-29T17:36:34.759124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:36:35.123077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16865
96.8%
1 551
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 16865
96.8%
1 551
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17416
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16865
96.8%
1 551
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 17416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16865
96.8%
1 551
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16865
96.8%
1 551
 
3.2%

weekend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.2 KiB
0
12436 
1
4980 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17416
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12436
71.4%
1 4980
28.6%

Length

2022-11-29T17:36:35.317619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:36:35.542694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12436
71.4%
1 4980
28.6%

Most occurring characters

ValueCountFrequency (%)
0 12436
71.4%
1 4980
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17416
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12436
71.4%
1 4980
28.6%

Most occurring scripts

ValueCountFrequency (%)
Common 17416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12436
71.4%
1 4980
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12436
71.4%
1 4980
28.6%

lockdown1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.2 KiB
0
16684 
1
 
732

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17416
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16684
95.8%
1 732
 
4.2%

Length

2022-11-29T17:36:35.721186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:36:36.002757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16684
95.8%
1 732
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 16684
95.8%
1 732
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17416
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16684
95.8%
1 732
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 17416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16684
95.8%
1 732
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16684
95.8%
1 732
 
4.2%

lockdown2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.2 KiB
0
15256 
1
2160 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17416
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15256
87.6%
1 2160
 
12.4%

Length

2022-11-29T17:36:36.259632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:36:36.532873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15256
87.6%
1 2160
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 15256
87.6%
1 2160
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17416
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15256
87.6%
1 2160
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 17416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15256
87.6%
1 2160
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15256
87.6%
1 2160
 
12.4%

season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.2 KiB
1
5844 
3
5807 
0
2921 
2
2844 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17416
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 5844
33.6%
3 5807
33.3%
0 2921
16.8%
2 2844
16.3%

Length

2022-11-29T17:36:36.783195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:36:37.033367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5844
33.6%
3 5807
33.3%
0 2921
16.8%
2 2844
16.3%

Most occurring characters

ValueCountFrequency (%)
1 5844
33.6%
3 5807
33.3%
0 2921
16.8%
2 2844
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17416
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5844
33.6%
3 5807
33.3%
0 2921
16.8%
2 2844
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 17416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5844
33.6%
3 5807
33.3%
0 2921
16.8%
2 2844
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5844
33.6%
3 5807
33.3%
0 2921
16.8%
2 2844
16.3%

is_night
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.2 KiB
0
10157 
1
7259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17416
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10157
58.3%
1 7259
41.7%

Length

2022-11-29T17:36:37.298782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:36:37.538072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10157
58.3%
1 7259
41.7%

Most occurring characters

ValueCountFrequency (%)
0 10157
58.3%
1 7259
41.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17416
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10157
58.3%
1 7259
41.7%

Most occurring scripts

ValueCountFrequency (%)
Common 17416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10157
58.3%
1 7259
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10157
58.3%
1 7259
41.7%

hour_sin
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00013185883
Minimum-0.99766877
Maximum0.99766877
Zeros728
Zeros (%)4.2%
Negative8706
Negative (%)50.0%
Memory size136.2 KiB
2022-11-29T17:36:37.782601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.99766877
5-th percentile-0.97908409
Q1-0.73083596
median0
Q30.63108794
95-th percentile0.97908409
Maximum0.99766877
Range1.9953375
Interquartile range (IQR)1.3619239

Descriptive statistics

Standard deviation0.69223038
Coefficient of variation (CV)-5249.7841
Kurtosis-1.4346213
Mean-0.00013185883
Median Absolute Deviation (MAD)0.73083596
Skewness-5.7033512 × 10-5
Sum-2.2964533
Variance0.4791829
MonotonicityNot monotonic
2022-11-29T17:36:38.046533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 728
 
4.2%
-0.9422609221 728
 
4.2%
0.51958395 727
 
4.2%
0.8878852184 727
 
4.2%
0.1361666491 727
 
4.2%
0.2697967712 727
 
4.2%
-0.816969893 726
 
4.2%
-0.2697967712 726
 
4.2%
0.9790840877 726
 
4.2%
-0.8878852184 726
 
4.2%
Other values (14) 10148
58.3%
ValueCountFrequency (%)
-0.9976687692 726
4.2%
-0.9790840877 726
4.2%
-0.9422609221 728
4.2%
-0.8878852184 726
4.2%
-0.816969893 726
4.2%
-0.7308359643 725
4.2%
-0.6310879443 724
4.2%
-0.51958395 723
4.2%
-0.3984010898 726
4.2%
-0.2697967712 726
4.2%
ValueCountFrequency (%)
0.9976687692 724
4.2%
0.9790840877 726
4.2%
0.9422609221 725
4.2%
0.8878852184 727
4.2%
0.816969893 725
4.2%
0.7308359643 725
4.2%
0.6310879443 725
4.2%
0.51958395 727
4.2%
0.3984010898 724
4.2%
0.2697967712 727
4.2%

hour_cos
Real number (ℝ)

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0419091
Minimum-0.99068595
Maximum1
Zeros0
Zeros (%)0.0%
Negative8705
Negative (%)50.0%
Memory size136.2 KiB
2022-11-29T17:36:38.314848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.99068595
5-th percentile-0.99068595
Q1-0.57668032
median0.20345601
Q30.8544194
95-th percentile1
Maximum1
Range1.9906859
Interquartile range (IQR)1.4310997

Descriptive statistics

Standard deviation0.72049846
Coefficient of variation (CV)17.191934
Kurtosis-1.5172311
Mean0.0419091
Median Absolute Deviation (MAD)0.70521233
Skewness-0.062753693
Sum729.88888
Variance0.51911804
MonotonicityNot monotonic
2022-11-29T17:36:38.573111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1453
 
8.3%
0.8544194045 1450
 
8.3%
0.6825531432 1450
 
8.3%
-0.3348796122 728
 
4.2%
0.4600650377 727
 
4.2%
0.9629172873 727
 
4.2%
-0.990685946 727
 
4.2%
-0.9172113015 726
 
4.2%
0.4600650377 726
 
4.2%
0.2034560131 726
 
4.2%
Other values (11) 7976
45.8%
ValueCountFrequency (%)
-0.990685946 725
4.2%
-0.990685946 727
4.2%
-0.9172113015 724
4.2%
-0.9172113015 726
4.2%
-0.7757112907 724
4.2%
-0.7757112907 725
4.2%
-0.5766803221 726
4.2%
-0.5766803221 725
4.2%
-0.3348796122 728
4.2%
-0.3348796122 725
4.2%
ValueCountFrequency (%)
1 1453
8.3%
0.9629172873 727
4.2%
0.9629172873 726
4.2%
0.8544194045 1450
8.3%
0.6825531432 1450
8.3%
0.4600650377 727
4.2%
0.4600650377 726
4.2%
0.2034560131 726
4.2%
0.2034560131 726
4.2%
-0.06824241336 726
4.2%

month_sin
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0028627867
Minimum-1
Maximum1
Zeros0
Zeros (%)0.0%
Negative8738
Negative (%)50.2%
Memory size136.2 KiB
2022-11-29T17:36:38.844643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-0.8660254
median-2.4492936 × 10-16
Q30.5
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)1.3660254

Descriptive statistics

Standard deviation0.70645103
Coefficient of variation (CV)-246.7704
Kurtosis-1.4967009
Mean-0.0028627867
Median Absolute Deviation (MAD)0.5
Skewness0.0023620223
Sum-49.858293
Variance0.49907306
MonotonicityNot monotonic
2022-11-29T17:36:39.094796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.5 2967
17.0%
-0.8660254038 1488
8.5%
-2.449293598 × 10-161488
8.5%
-0.5 1486
8.5%
-0.8660254038 1480
8.5%
1 1474
8.5%
-1 1440
8.3%
1.224646799 × 10-161438
8.3%
0.8660254038 1434
8.2%
0.8660254038 1365
7.8%
ValueCountFrequency (%)
-1 1440
8.3%
-0.8660254038 1488
8.5%
-0.8660254038 1480
8.5%
-0.5 1356
7.8%
-0.5 1486
8.5%
-2.449293598 × 10-161488
8.5%
1.224646799 × 10-161438
8.3%
0.5 2967
17.0%
0.8660254038 1365
7.8%
0.8660254038 1434
8.2%
ValueCountFrequency (%)
1 1474
8.5%
0.8660254038 1434
8.2%
0.8660254038 1365
7.8%
0.5 2967
17.0%
1.224646799 × 10-161438
8.3%
-2.449293598 × 10-161488
8.5%
-0.5 1486
8.5%
-0.5 1356
7.8%
-0.8660254038 1480
8.5%
-0.8660254038 1488
8.5%

month_cos
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0056927814
Minimum-1
Maximum1
Zeros0
Zeros (%)0.0%
Negative8765
Negative (%)50.3%
Memory size136.2 KiB
2022-11-29T17:36:39.368176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-0.8660254
median-1.8369702 × 10-16
Q30.5
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)1.3660254

Descriptive statistics

Standard deviation0.70777381
Coefficient of variation (CV)-124.3283
Kurtosis-1.5001526
Mean-0.0056927814
Median Absolute Deviation (MAD)0.8660254
Skewness0.016583533
Sum-99.14548
Variance0.50094376
MonotonicityNot monotonic
2022-11-29T17:36:39.589361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.5 1488
8.5%
1 1488
8.5%
-0.8660254038 1487
8.5%
-0.8660254038 1486
8.5%
0.8660254038 1480
8.5%
-0.5 1480
8.5%
6.123233996 × 10-171474
8.5%
-1.836970199 × 10-161440
8.3%
-1 1438
8.3%
-0.5 1434
8.2%
Other values (2) 2721
15.6%
ValueCountFrequency (%)
-1 1438
8.3%
-0.8660254038 1486
8.5%
-0.8660254038 1487
8.5%
-0.5 1480
8.5%
-0.5 1434
8.2%
-1.836970199 × 10-161440
8.3%
6.123233996 × 10-171474
8.5%
0.5 1488
8.5%
0.5 1365
7.8%
0.8660254038 1356
7.8%
ValueCountFrequency (%)
1 1488
8.5%
0.8660254038 1480
8.5%
0.8660254038 1356
7.8%
0.5 1365
7.8%
0.5 1488
8.5%
6.123233996 × 10-171474
8.5%
-1.836970199 × 10-161440
8.3%
-0.5 1434
8.2%
-0.5 1480
8.5%
-0.8660254038 1487
8.5%

Interactions

2022-11-29T17:36:23.948464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:45.396960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:49.304601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:52.986484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:57.171940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:00.641456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:04.248344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:07.542254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:11.613103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:15.585401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:19.576569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:24.234317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:45.680474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:49.584697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:53.364554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:57.451103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:00.926094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:04.522461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:08.282327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:11.935297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:15.986330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:19.923965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:24.574500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:46.022839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:49.911240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:53.719181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:57.727652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:01.250828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:04.827729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:08.586814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:12.246194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:16.309782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:20.902636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:24.875848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:46.361944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:50.251905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:53.943824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:58.030242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:01.558466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:05.087108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:08.932524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:12.627080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:16.660068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:21.229920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:25.187153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:46.800612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:50.578719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:54.311912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:58.334565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:01.885783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:05.345566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:09.257403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:12.935632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:17.023490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:21.595503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:25.544715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:47.248447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:50.919427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:54.617748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:58.625620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:02.206218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:05.684109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:09.591708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:13.250953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:17.360007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:21.868992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:25.787246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:47.554512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:51.204096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:54.873002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:58.885368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:02.556580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:06.008069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:09.919065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:13.646031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:17.678103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:22.212668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:26.161944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:47.880303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:51.486601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:55.139049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:59.252773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:02.946247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:06.329977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:10.259777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:14.038861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:17.997200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:22.558893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:26.458863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:48.180133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:51.878447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:55.475433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:59.604814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:03.343294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:06.592610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:10.608395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:14.445230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:18.409945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:22.912698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:26.788795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:48.680504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:52.189878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:56.563055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:59.903112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:03.660769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:06.936926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:10.997688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:14.818443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:18.843824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:23.251623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:27.164510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:48.983542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:52.548227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:35:56.867364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:00.234777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:03.931402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:07.282439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:11.299011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:15.229218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:19.286677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-29T17:36:23.592587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-11-29T17:36:40.382726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T17:36:40.858840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T17:36:41.486625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T17:36:42.034260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T17:36:42.466650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T17:36:42.785025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T17:36:27.625898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T17:36:28.274252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datetempdwptrhumprcpwdirwspdpresholidayweekendlockdown1lockdown2seasonis_nighthour_sinhour_cosmonth_sinmonth_cos
02020-01-01 00:00:000.8-0.1940.0170.05.41032.51000310.0000001.0000000.50.866025
12020-01-01 02:00:00-0.7-1.3960.0200.09.41032.21000310.2697970.9629170.50.866025
22020-01-01 04:00:00-0.3-0.6980.0190.07.61032.21000310.5195840.8544190.50.866025
32020-01-01 06:00:000.0-0.3980.0220.05.41031.91000310.7308360.6825530.50.866025
42020-01-01 08:00:000.30.0980.0210.07.61031.61000310.8878850.4600650.50.866025
52020-01-01 10:00:000.50.2980.0170.03.61031.91000310.9790840.2034560.50.866025
62020-01-01 12:00:000.70.4980.0160.05.41031.91000300.997669-0.0682420.50.866025
72020-01-01 14:00:000.80.5980.0170.011.21031.91000300.942261-0.3348800.50.866025
82020-01-01 16:00:001.00.7980.0160.011.21032.21000300.816970-0.5766800.50.866025
92020-01-01 18:00:001.41.3990.0160.011.21032.51000300.631088-0.7757110.50.866025
datetempdwptrhumprcpwdirwspdpresholidayweekendlockdown1lockdown2seasonis_nighthour_sinhour_cosmonth_sinmonth_cos
174062022-01-01 04:00:0015.210.6740.0220.09.41023.1000030-6.310879e-01-0.775711-2.449294e-161.0
174072022-01-01 06:00:0015.411.0750.0210.07.61023.6000030-8.169699e-01-0.576680-2.449294e-161.0
174082022-01-01 08:00:0014.410.8790.0220.011.21024.3000030-9.422609e-01-0.334880-2.449294e-161.0
174092022-01-01 10:00:0013.310.8850.0220.07.61025.1000030-9.976688e-01-0.068242-2.449294e-161.0
174102022-01-01 12:00:0012.910.8870.0200.09.41025.5000030-9.790841e-010.203456-2.449294e-161.0
174112022-01-01 14:00:0011.910.3900.0250.05.41026.5000030-8.878852e-010.460065-2.449294e-161.0
174122022-01-01 16:00:0012.010.6910.0280.011.21026.6000031-7.308360e-010.682553-2.449294e-161.0
174132022-01-01 18:00:0012.210.9920.0280.07.61026.6000031-5.195840e-010.854419-2.449294e-161.0
174142022-01-01 20:00:0012.010.6910.0270.03.61026.7000031-2.697968e-010.962917-2.449294e-161.0
174152022-01-01 22:00:0011.810.5920.0190.09.41026.6000031-2.449294e-161.000000-2.449294e-161.0